More types

.github/workflows/style.yml

@@ -1,4 +1,4 @@
-name: Code Style Checks
+name: style
 
 on:
   push:
@@ -7,7 +7,7 @@ on:
     branches: [ master ]
 
 jobs:
-  build:
+  flake8:
     runs-on: 'ubuntu-latest'
     steps:
     - uses: actions/checkout@v4
@@ -24,3 +24,21 @@ jobs:
     - name: pt.java.required checks
       run: |
         flake8 ./pyterrier --select=PT --show-source --statistics --count
+
+  mypy:
+    runs-on: 'ubuntu-latest'
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Install
+      run: |
+        pip install mypy --upgrade -r requirements.txt -r requirements-test.txt
+        pip install -e .
+
+    - name: MyPy
+      run: 'mypy --disable-error-code=import-untyped pyterrier || true'

pyterrier/__init__.py

@@ -31,6 +31,8 @@
 
 # will be set in terrier.terrier.java once java is loaded
 IndexRef = None
+# will be set in once utils.set_tqdm() once _() runs
+tqdm = None
 
 
 # deprecated functions explored to the main namespace, which will be removed in a future version

pyterrier/_ops.py

@@ -32,7 +32,7 @@ def __len__(self) -> int:
 
 def _flatten(transformers: Iterable[Transformer], cls: type) -> Tuple[Transformer]:
     return list(chain.from_iterable(
-        (t._transformers if isinstance(t, cls) else [t])
+        (t._transformers if isinstance(t, cls) else [t]) # type: ignore
         for t in transformers
     ))
 
@@ -193,6 +193,7 @@ def fuse_left(self, left: Transformer) -> Optional[Transformer]:
         # If the preceding component supports a native rank cutoff (via fuse_rank_cutoff), apply it.
         if isinstance(left, SupportsFuseRankCutoff):
             return left.fuse_rank_cutoff(self.k)
+        return None
 
 class FeatureUnion(NAryTransformerBase):
     """
@@ -295,7 +296,7 @@ def compile(self) -> Transformer:
         """
             Returns a new transformer that fuses feature unions where possible.
         """
-        out = deque()
+        out : deque = deque()
         inp = deque([t.compile() for t in self._transformers])
         while inp:
             right = inp.popleft()
@@ -382,7 +383,7 @@ def compile(self, verbose: bool = False) -> Transformer:
         """Returns a new transformer that iteratively fuses adjacent transformers to form a more efficient pipeline."""
         # compile constituent transformers (flatten allows complie() to return Compose pipelines)
         inp = deque(_flatten((t.compile() for t in self._transformers), Compose))
-        out = deque()
+        out : deque = deque()
         counter = 1
         while inp:
             if verbose:

pyterrier/apply_base.py

@@ -1,7 +1,7 @@
 from typing import Callable, Any, Union, Optional, Iterable
 import itertools
 import more_itertools
-import numpy as np
+import numpy.typing as npt
 import pandas as pd
 import pyterrier as pt
 
@@ -92,7 +92,7 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
         # batching
         iterator = pt.model.split_df(inp, batch_size=self.batch_size)
         if self.verbose:
-            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') 
+            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') # type: ignore 
         return pd.concat([self._apply_df(chunk_df) for chunk_df in iterator])
 
     def _apply_df(self, inp: pd.DataFrame) -> pd.DataFrame:
@@ -148,7 +148,7 @@ def transform(self, res: pd.DataFrame) -> pd.DataFrame:
         it = res.groupby("qid")
         lastqid = None
         if self.verbose:
-            it = pt.tqdm(it, unit='query')
+            it = pt.tqdm(it, unit='query') # type: ignore
         try:
             if self.batch_size is None:
                 query_dfs = []
@@ -163,7 +163,7 @@ def transform(self, res: pd.DataFrame) -> pd.DataFrame:
                     iterator = pt.model.split_df(group, batch_size=self.batch_size)
                     query_dfs.append( pd.concat([self.fn(chunk_df) for chunk_df in iterator]) )
         except Exception as a:
-            raise Exception("Problem applying %s for qid %s" % (self.fn, lastqid)) from a
+            raise Exception("Problem applying %r for qid %s" % (self.fn, lastqid)) from a # %r because its a function with bytes representation (mypy)
 
         if self.add_ranks:
             try:
@@ -253,7 +253,7 @@ def __repr__(self):
     def _transform_rowwise(self, outputRes):
         if self.verbose:
             pt.tqdm.pandas(desc="pt.apply.doc_score", unit="d")
-            outputRes["score"] = outputRes.progress_apply(self.fn, axis=1).astype('float64')
+            outputRes["score"] = outputRes.progress_apply(self.fn, axis=1).astype('float64') # type: ignore
         else:
             outputRes["score"] = outputRes.apply(self.fn, axis=1).astype('float64')
         outputRes = pt.model.add_ranks(outputRes)
@@ -275,7 +275,7 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
 
         iterator = pt.model.split_df(outputRes, batch_size=self.batch_size)
         if self.verbose:
-            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row')
+            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') # type: ignore
         rtr = pd.concat([self._transform_batchwise(chunk_df) for chunk_df in iterator])
         rtr = pt.model.add_ranks(rtr)
         return rtr
@@ -294,7 +294,7 @@ def _feature_fn(row):
             pipe = pt.terrier.Retriever(index) >> pt.apply.doc_features(_feature_fn) >> pt.LTRpipeline(xgBoost())
     """
     def __init__(self,
-        fn: Callable[[Union[pd.Series, pt.model.IterDictRecord]], np.array],
+        fn: Callable[[Union[pd.Series, pt.model.IterDictRecord]], npt.NDArray],
         *,
         verbose: bool = False
     ):
@@ -313,7 +313,7 @@ def transform_iter(self, inp: pt.model.IterDict) -> pt.model.IterDict:
         # we assume that the function can take a dictionary as well as a pandas.Series. As long as [""] notation is used
         # to access fields, both should work
         if self.verbose:
-            inp = pt.tqdm(inp, desc="pt.apply.doc_features")
+            inp = pt.tqdm(inp, desc="pt.apply.doc_features") # type: ignore
         for row in inp:
             row["features"] = self.fn(row)
             yield row
@@ -322,8 +322,8 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
         fn = self.fn
         outputRes = inp.copy()
         if self.verbose:
-            pt.tqdm.pandas(desc="pt.apply.doc_features", unit="d")
-            outputRes["features"] = outputRes.progress_apply(fn, axis=1)
+            pt.tqdm.pandas(desc="pt.apply.doc_features", unit="d") # type: ignore
+            outputRes["features"] = outputRes.progress_apply(fn, axis=1) # type: ignore
         else:
             outputRes["features"] = outputRes.apply(fn, axis=1)
         return outputRes
@@ -368,7 +368,7 @@ def transform_iter(self, inp: pt.model.IterDict) -> pt.model.IterDict:
         # we assume that the function can take a dictionary as well as a pandas.Series. As long as [""] notation is used
         # to access fields, both should work
         if self.verbose:
-            inp = pt.tqdm(inp, desc="pt.apply.query")
+            inp = pt.tqdm(inp, desc="pt.apply.query") # type: ignore
         for row in inp:
             row = row.copy()
             if "query" in row:
@@ -384,8 +384,8 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
             outputRes = inp.copy()
         try:
             if self.verbose:
-                pt.tqdm.pandas(desc="pt.apply.query", unit="d")
-                outputRes["query"] = outputRes.progress_apply(self.fn, axis=1)
+                pt.tqdm.pandas(desc="pt.apply.query", unit="d") # type: ignore
+                outputRes["query"] = outputRes.progress_apply(self.fn, axis=1) # type: ignore
             else:
                 outputRes["query"] = outputRes.apply(self.fn, axis=1)
         except ValueError as ve:
@@ -444,7 +444,7 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
         # batching
         iterator = pt.model.split_df(inp, batch_size=self.batch_size)
         if self.verbose:
-            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') 
+            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') # type: ignore
         rtr = pd.concat([self.fn(chunk_df) for chunk_df in iterator])
         return rtr
 

pyterrier/datasets.py

@@ -3,8 +3,9 @@
 import json
 import pandas as pd
 from .transformer import is_lambda
+from abc import abstractmethod
 import types
-from typing import Union, Tuple, Iterator, Dict, Any, List, Literal
+from typing import Union, Tuple, Iterator, Dict, Any, List, Literal, Optional
 from warnings import warn
 import requests
 from .io import autoopen, touch
@@ -54,12 +55,13 @@ def get_corpus(self):
         """
         pass
 
+    @abstractmethod
     def get_corpus_iter(self, verbose=True) -> pt.model.IterDict:
         """
             Returns an iter of dicts for this collection. If verbose=True, a tqdm pbar shows the progress over this iterator.
         """
         pass
-
+    
     def get_corpus_lang(self) -> Union[str,None]:
         """
             Returns the ISO 639-1 language code for the corpus, or None for multiple/other/unknown
@@ -72,6 +74,7 @@ def get_index(self, variant=None, **kwargs):
         """
         pass
 
+    @abstractmethod
     def get_topics(self, variant=None) -> pd.DataFrame:
         """
             Returns the topics, as a dataframe, ready for retrieval. 
@@ -84,6 +87,7 @@ def get_topics_lang(self) -> Union[str,None]:
         """
         return None
 
+    @abstractmethod
     def get_qrels(self, variant=None) -> pd.DataFrame:
         """ 
             Returns the qrels, as a dataframe, ready for evaluation.
@@ -109,7 +113,7 @@ def get_results(self, variant=None) -> pd.DataFrame:
         """ 
             Returns a standard result set provided by the dataset. This is useful for re-ranking experiments.
         """
-        pass
+        return None
 
 class RemoteDataset(Dataset):
 
@@ -139,7 +143,7 @@ def download(URLs : Union[str,List[str]], filename : str, **kwargs):
                 r = requests.get(url, allow_redirects=True, stream=True, **kwargs)
                 r.raise_for_status()
                 total = int(r.headers.get('content-length', 0))
-                with pt.io.finalized_open(filename, 'b') as file, pt.tqdm(
+                with pt.io.finalized_open(filename, 'b') as file, pt.tqdm( # type: ignore
                         desc=basename,
                         total=total,
                         unit='iB',
@@ -507,7 +511,7 @@ def get_results(self, variant=None) -> pd.DataFrame:
         result.sort_values(by=['qid', 'score', 'docno'], ascending=[True, False, True], inplace=True) # ensure data is sorted by qid, -score, did
         # result doesn't yet contain queries (only qids) so load and merge them in
         topics = self.get_topics(variant)
-        result = pd.merge(result, topics, how='left', on='qid', copy=False)
+        result = pd.merge(result, topics, how='left', on='qid')
         return result
 
     def _describe_component(self, component):
@@ -610,7 +614,7 @@ def transform(self, inp: pd.DataFrame) -> pd.DataFrame:
         set_docnos = set(docnos)
         it = (tuple(getattr(doc, f) for f in fields) for doc in docstore.get_many_iter(set_docnos))
         if self.verbose:
-            it = pd.tqdm(it, unit='d', total=len(set_docnos), desc='IRDSTextLoader')
+            it = pt.tqdm(it, unit='d', total=len(set_docnos), desc='IRDSTextLoader') # type: ignore
         metadata = pd.DataFrame(list(it), columns=fields).set_index('doc_id')
         metadata_frame = metadata.loc[docnos].reset_index(drop=True)
 
@@ -1104,7 +1108,7 @@ def _merge_years(self, component, variant):
     "corpus_iter" : lambda dataset, **kwargs : pt.index.treccollection2textgen(dataset.get_corpus(), num_docs=11429, verbose=kwargs.get("verbose", False))
 }
 
-DATASET_MAP = {
+DATASET_MAP : Dict[str, Dataset] = {
     # used for UGlasgow teaching
     "50pct" : RemoteDataset("50pct", FIFTY_PCT_FILES),
     # umass antique corpus - see http://ciir.cs.umass.edu/downloads/Antique/ 
@@ -1222,7 +1226,7 @@ def list_datasets(en_only=True):
 def transformer_from_dataset(
     dataset : Union[str, Dataset],
     clz,
-    variant: str = None,
+    variant: Optional[str] = None,
     version: str = 'latest',        
     **kwargs) -> pt.Transformer:
     """Returns a Transformer instance of type ``clz`` for the provided index of variant ``variant``."""

pyterrier/debug.py

@@ -1,7 +1,7 @@
 from . import Transformer
-from typing import List
+from typing import List, Optional
 
-def print_columns(by_query : bool = False, message : str = None) -> Transformer:
+def print_columns(by_query : Optional[bool] = False, message : Optional[str] = None) -> Transformer:
     """
     Returns a transformer that can be inserted into pipelines that can print the column names of the dataframe
     at this stage in the pipeline:
@@ -82,8 +82,8 @@ def print_rows(
         by_query : bool = True, 
         jupyter: bool = True, 
         head : int = 2, 
-        message : str = None, 
-        columns : List[str] = None) -> Transformer:
+        message : Optional[str] = None, 
+        columns : Optional[List[str]] = None) -> Transformer:
     """
     Returns a transformer that can be inserted into pipelines that can print some of the dataframe
     at this stage in the pipeline:

pyterrier/java/_core.py

@@ -1,3 +1,4 @@
+# type: ignore
 import os
 from pyterrier.java import required_raise, required, before_init, started, mavenresolver, JavaClasses, JavaInitializer, register_config
 from typing import Optional
@@ -153,7 +154,7 @@ def add_jar(jar_path):
 
 
 @before_init
-def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
+def add_package(org_name : str, package_name : str, version : Optional[str] = None, file_type : str = 'jar'):
     if version is None or version == 'snapshot':
         version = mavenresolver.latest_version_num(org_name, package_name)
     file_name = mavenresolver.get_package_jar(org_name, package_name, version, artifact=file_type)

pyterrier/java/_utils.py

@@ -1,3 +1,4 @@
+# type: ignore
 import sys
 import warnings
 from functools import wraps
@@ -387,7 +388,7 @@ def register_config(name, config: Dict[str, Any]):
 class JavaClasses:
     def __init__(self, **mapping: Union[str, Callable[[], str]]):
         self._mapping = mapping
-        self._cache = {}
+        self._cache : Dict[str, Callable]= {}
 
     def __dir__(self):
         return list(self._mapping.keys())

pyterrier/model.py

@@ -239,6 +239,7 @@ def split_df(df : pd.DataFrame, N: Optional[int] = None, *, batch_size: Optional
     assert (N is None) != (batch_size is None), "Either N or batch_size should be provided (and not both)"
 
     if N is None:
+        assert batch_size is not None
         N = math.ceil(len(df) / batch_size)
 
     type = None

pyterrier/new.py

@@ -1,5 +1,5 @@
 
-from typing import Sequence, Union
+from typing import Sequence, Union, Optional, cast, Iterable
 import pandas as pd
 from .model import add_ranks
 
@@ -9,7 +9,7 @@ def empty_Q() -> pd.DataFrame:
     """
     return pd.DataFrame(columns=["qid", "query"])
 
-def queries(queries : Union[str, Sequence[str]], qid : Union[str, Sequence[str]] = None, **others) -> pd.DataFrame:
+def queries(queries : Union[str, Sequence[str]], qid : Optional[Union[str, Iterable[str]]] = None, **others) -> pd.DataFrame:
     """
         Creates a new queries dataframe. Will return a dataframe with the columns `["qid", "query"]`. 
         Any further lists in others will also be added.
@@ -40,7 +40,7 @@ def queries(queries : Union[str, Sequence[str]], qid : Union[str, Sequence[str]]
         assert type(qid) == str
         return pd.DataFrame({"qid" : [qid], "query" : [queries], **others})
     if qid is None:
-        qid = map(str, range(1, len(queries)+1))
+        qid = cast(Iterable[str], map(str, range(1, len(queries)+1))) # noqa: PT100 (this is typing.cast, not jinus.cast)
     return pd.DataFrame({"qid" : qid, "query" : queries, **others})
 
 Q = queries
@@ -53,8 +53,8 @@ def empty_R() -> pd.DataFrame:
 
 def ranked_documents(
         scores : Sequence[Sequence[float]], 
-        qid : Sequence[str] = None, 
-        docno=None, 
+        qid : Optional[Sequence[str]] = None, 
+        docno : Optional[Sequence[Sequence[str]]] = None, 
         **others) -> pd.DataFrame:
     """
         Creates a new ranked documents dataframe. Will return a dataframe with the columns `["qid", "docno", "score", "rank"]`. 
@@ -120,4 +120,4 @@ def ranked_documents(
         raise ValueError("We assume multiple documents, for now")
     return add_ranks(rtr)
 
-R = ranked_documents
+R = ranked_documents